Classifast.com

Accurate classifier of UNSPSC, NAICS, HS/CN/HTS codes, NACE, EMDN, ETIM and more

Get the right category codes from the most widely used classification standards.

Classifast is a web application that provides easy classification of any text input according to international product and service standards like UNSPSC, NAICS, ISIC, ETIM, HS, CN. Built with FastAPI and modern web technologies, it offers fast, accurate semantic search capabilities for automated yet intelligent categorization.

Features

• Fast Classification: Semantic search using advanced embedding models
• High Accuracy: Confidence scores for each classification result
• Multiple Standards: Support for UNSPSC, ETIM, and NAICS classification standards
• Modern Interface: Clean, responsive design built with Tailwind CSS
• SEO Optimized: Structured data, meta tags, and performance optimized

Supported classification standards (Top 3)

UNSPSC (United Nations Standard Products and Services Codes)

• Global standard for product and service categorization
• Improves spend analytics and procurement processes
• Version: UNv260801 (August 14, 2023)

ETIM (European Technical Information Model)

• B2B open standard for technical product classification
• Specialized for electrical and technical products
• Version: 10.0 (2024-12-10)

NAICS 2022 (North American Industry Classification System)

• Industry classification for business activities
• Essential for government contracting and reporting

Stack

• Backend: FastAPI with Python
• Frontend: TypeScript, Tailwind CSS, HTMX
• Vector Database: Qdrant for semantic search
• Embedding Models: Hugging Face Inference with Qwen embeddings
• Deployment: Docker containerized

Demo

Visit the working instance at classifast.com to try.

Installation

1. Clone the repository
2. Install Python dependencies: pip install -r requirements.txt
3. Install frontend dependencies: npm install
4. Set environment variables for embedding inference, API keys, and payments/quota behavior
5. Run frontend watchers and the app: npm run dev

Relevant embedding environment variables:

• HF_TOKEN
• HF_INFERENCE_PROVIDER defaults to auto
• HF_EMBEDDING_MODEL defaults to Qwen/Qwen3-Embedding-8B
• HF_EMBEDDING_DIMS defaults to 2048

Reranking is served by OpenRouter and uses its own API key and model:

• OPENROUTER_API_KEY
• OPENROUTER_RERANK_MODEL defaults to voyageai/rerank-3
• OPENROUTER_RERANK_TIMEOUT_SECONDS defaults to 30

Embedding inference uses the configured HF_INFERENCE_PROVIDER. Reranking is sent to OpenRouter's /api/v1/rerank endpoint with the OPENROUTER_API_KEY.

The classifier page also has an off-by-default Expand query beta switch. When enabled, google/gemini-3.1-flash-lite adds a short description after the original query for embedding and reranking. The beta model inputs put each classification instruction after that semantic text. Exact ID matches skip the model call. Code-like queries also skip it after an exact miss. The original text stays in the input, result heading, URL, and ID lookup. If OpenRouter is unavailable or the API key is absent, classification uses the original text and the ordinary instruction-first format. Provider failures are not cached. Shared beta searches use ?enhance_query=1 in the URL.

Relevant Qdrant environment variables:

• QDRANT_URL is preferred when set. Full HTTP(S) URLs are preserved; a bare hostname is treated as HTTPS.
• QDRANT_HOST and QDRANT_PORT (default localhost:6333) are the fallback when QDRANT_URL is unset.
• QDRANT_API_KEY is optional for unprotected local Qdrant deployments.

Relevant payment and quota environment variables:

• POLAR_ACCESS_TOKEN
• POLAR_WEBHOOK_SECRET
• POLAR_PRO_PRODUCT_ID
• QUOTA_FAIL_OPEN defaults to true
• CHECKOUT_GRACE_TTL defaults to 300

Frontend JS/CSS files under app/static/ are build artifacts. They are generated from app/assets/ and are intentionally not committed to git.

Deployment

For production, deploy from the repo root with the provided Dockerfile. The image builds the frontend assets in a Node/npm builder stage and copies only the compiled files into the final Python runtime image.

For Coolify, use:

• Source directory: repo root
• Build pack: Dockerfile
• Dockerfile path: ./Dockerfile
• Include Source Commit in Build: disabled

docker-compose.yaml can still be used for local or manual container runs, but the recommended Coolify production path is the Dockerfile build directly.

Application startup validates every configured Qdrant collection, vector size, and required payload index without modifying Qdrant. Startup fails when the schema contract is invalid. Prepare and verify Qdrant explicitly before a deployment:

source .venv/bin/activate
python utilities/sync_payload_indexes.py apply
python utilities/sync_payload_indexes.py check

apply is a live migration operation: it backfills normalized ID payloads and creates or replaces payload indexes. check is read-only. Run apply from a controlled maintenance environment, verify the resolved Qdrant target before confirming the operation, then deploy only after check succeeds. The utility loads the repository .env; values already exported by the shell or supplied by the container take precedence. A Qdrant client cleanup failure is reported as an operational failure and makes the command exit nonzero.

Synchronous classification stages run on one dedicated background worker per application process. Each process admits one active classification and up to four waiting classifications. When the queue is full, fragment and RapidAPI requests receive HTTP 503. Fragment callers already over quota receive the paywall instead, without taking a queue slot. Server-rendered pages still return 200 to visitors and load results in the browser, while verified Google crawlers receive HTTP 503 with Retry-After. An active classification retains its turn through query enhancement and every synchronous stage. This bounds queued work and keeps health checks, webhooks, and cached-page handling responsive.

API

• GET / Homepage
• GET /{classifier_type} Classification page (unspsc, etim, naics)
• POST /{classifier_type} Submit classification request
• GET /health Health check endpoint

Testing

Automated backend tests live under tests/ and use pytest as the test runner. The existing tests retain their unittest.TestCase-compatible structure.

• Run the backend suite: pytest
• Run the frontend typecheck: npm run typecheck
• Run the Python typecheck: python -m mypy

utilities/test_*.py scripts are manual smoke/debug helpers for live integrations and are intentionally separate from the main regression suite.

SEO

• Structured data markup (JSON-LD)
• FAQ schema for common questions
• Optimized meta descriptions and titles
• Breadcrumb navigation
• Semantic HTML structure
• Performance optimized loading